#AI testing automation
737 related articles
·2 min
6-Day Open Source AI Agent: Natural Language-Driven Android Automation Testing
Veta is an open source AI testing agent: just describe your test goal in natural language and it autonomously plans, executes, verifies, and reports Android test results — no scripts needed.
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·2 min
Cursor + Appium: Building a Full Mobile Automation Testing Workflow with AI
Learn how to combine Cursor AI with Appium in a 4-step workflow—environment check, test planning, project generation, and reporting—to build mobile automation fast.
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·3 min
AI Testing End-to-End in Practice: A Complete Guide from Test Case Generation to Agent Automation
A deep dive into AI testing workflows: API capture, test case generation, Agent orchestration, and automated execution. Learn the two core challenges — incomplete information and mandatory human review.
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·6 min
Playwright E2E Builder: Engineering AI-Driven Web Automation Testing
Playwright E2E Builder is an AI Skill installed in Cursor that transforms UI automation from throwaway scripts into sustainable engineering assets through a four-step workflow, with built-in locator health checks.
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·6 min
Semi-AI Mode: A Practical Guide to Implementing API Automation Testing
Can't make pure AI work? This guide explores the Semi-AI approach to API automation testing, covering key challenges, enterprise framework design, and how AI and frameworks work together for maximum impact.
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·2 min
AI-Powered Playwright: A Beginner's Guide to Zero-Code Web Automation Testing
Compare Playwright vs Selenium, explore Playwright's DevTools Protocol speed advantages, smart locators, and AI integration via MCP for zero-code web automation testing.
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·3 min
AI-Powered Playwright: A Practical Guide to Zero-Code Web Automation Testing
Explore Playwright's core advantages including DevTools Protocol architecture, smart locators, and high-concurrency support, plus how to achieve zero-code automation testing with AI via MCP.
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Tutorials·4 min
Semi-AI Mode: A Pragmatic Approach to API Automation Testing Frameworks
Explore the semi-AI approach to API automation testing: why pure AI fails, framework design principles, technology choices, and clear human-AI division of labor for practical implementation.
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·3 min
Self-Hosting Kimi K3: Is Spending 20% More on Hardware Worth a 20% Improvement in Task Performance?
Analysis of whether spending 20% more on hardware for self-hosting Kimi K3 to gain 20% task performance improvement is worthwhile, covering inference precision, VRAM optimization, and tiered deployment.
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·3 min
Verification Browser for AI Agents: How 13ms Ultra-Fast Validation Solves the Trust Problem in Automation
Deep dive into the verification browser for AI agents: how 13ms verification windows and one-call checks solve hallucination problems in browser automation, enabling the leap from capability to trustworthiness.
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·3 min
Deep Analysis and Hardening Practices for 16 Critical Vulnerabilities in an AI Security Platform
An AI security platform was found to have 16 critical vulnerabilities spanning prompt injection, privilege escalation, and auth bypass. A deep dive into hardening methodologies.
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·4 min
Transitioning from DevOps to MLOps: A Complete Learning Roadmap
How can DevOps engineers efficiently transition to MLOps? This guide covers MLOps core concepts, standard workflows, essential tools, and Azure practices with a progressive learning roadmap.
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·3 min
U.S. Ban Targets Chinese Humanoid Robots: A New Front in the AI Race
Analysis of the U.S. ban on Chinese humanoid robots: data security concerns, industrial protection motives, and how the AI race extends into Physical AI and robotics hardware.
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·3 min
U.S. Ban on Chinese Humanoid Robots: A New Front in the AI Race
Analysis of the U.S. ban on Chinese humanoid robots: data security concerns, industrial protection motives, and how the AI race extends into Physical AI and robotics hardware.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·2 min
OpenAI Open-Sources Codex Security Components: Setting a New Standard for AI Coding Safety
OpenAI open-sources Codex Security components, bringing automated security detection to AI code generation. Analysis of its strategic value, developer impact, and the industry shift from capability to security.
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·2 min
OpenAI Open-Sources Codex Security Components: Setting a New Standard for AI Coding Safety
OpenAI open-sources Codex Security components, bringing automated security detection to AI code generation. Analysis of its strategic significance and industry impact.
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·3 min
Shell Colon Command Explained: The Art of "Doing Nothing" in Bash
Deep dive into Bash's colon command (:) and its practical uses: no-op placeholders, infinite loops, default value assignment. Explores Bash design philosophy and PowerShell comparison.
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·4 min
What It Feels Like When Production Actually Runs Like Localhost
Why does production never match local? This article analyzes root causes like config gaps and dependency drift, and explores how Docker, Twelve-Factor App, and IaC practices bridge the dev-prod divide.
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